Markov Random Field (MRF) or Conditional Random Field (CRF) has achieved great successes in semantic image labeling, which is one of the most challenging problems in computer vision. Existing works can be generally categorized into two groups based on their definitions of the unary and pairwise terms of MRF.
In the first group, researchers improved labeling accuracy by exploring rich information to define the pairwise functions, including long-range dependencies, high-order potentials, and semantic label contexts.
In the second group, people learned a strong unary classifier by leveraging the recent advances of deep learning, such as the Convolutional Neural Network (CNN). With deep models, these works demonstrated encouraging results using simple definition of the pairwise function or even ignore it.